Semi-supervised learning techniques in artificial olfaction: A novel approach to classification problems and drift counteraction

Saverio De Vito, Grazia Fattoruso, Matteo Pardo, Francesco Tortorella, Girolamo Di Francia

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

Semi-supervised learning is a promising research area aiming to develop pattern recognition tools capable to exploit simultaneously the benefits from supervised and unsupervised learning techniques. These can lead to a very efficient usage of the limited number of supervised samples achievable in many artificial olfaction problems like distributed air quality monitoring. We believe it can also be beneficial in addressing another source of limited knowledge we have to face when dealing with real world problems: concept and sensor drifts. In this paper we describe the results of two artificial olfaction investigations that show semi-supervised learning techniques capabilities to boost performance of state-of-the art classifiers and regressors. The use of semi-supervised learning approach resulted in the effective reduction of drift-induced performance degradation in long-term on-field continuous operation of chemical multisensory devices. © 2012 IEEE.
Original languageEnglish
Article number6176193
Pages (from-to)3215 - 3224
Number of pages10
JournalIEEE Sensors Journal
Volume12
Issue number11
DOIs
Publication statusPublished - 2012

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All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering

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